Large language models pretrained on massive amounts of data have led
to remarkable progress in recent years, with models like
BART [1]}, GPT [2]}, [3]}, and T5 [4]} yielding
huge improvements for a vast number of text generation tasks. Inspired
by this, a new research initiative has emerged around building large
models that are pretrained on source code and technical text to
address software-related tasks. This includes models like
PLBART [5]},
CodeGPT-2 [6]}, and
CodeT5 [7]}. While these models demonstrate
impressive performance on generation tasks like code summarization,
code generation, and code translation,
it is unclear if they are well-suited for the editing nature of many software-related tasks.
For instance, bug fixing  [8]} entails editing source
code to resolve bugs, automated code review  [9]} requires editing source
code to incorporate feedback from review comments, and
comment updating  [10]} pertains to updating
outdated natural language comments to reflect code changes.
